Testing
Reasoning models — OpenAI's o3/o4-mini, Anthropic's Claude with extended thinking, and DeepSeek R1 — require different evaluation strategies than standard language models. They trade token budget for reasoning quality, produce verifiable intermediate steps, and have different failure modes. This guide covers building eval frameworks specifically for reasoning
Testing
DeepSeek R1 is an open-weight reasoning model that exposes its chain-of-thought reasoning as a <think> block before the final answer. This transparency makes it uniquely testable — you can evaluate not just whether the answer is correct, but whether the reasoning process is sound. This guide covers testing strategies
Testing
Gemini 2.5 Pro is Google's most capable model in 2026, with native multimodal input (images, audio, video, documents), a 2-million-token context window, and an optional "thinking" mode for complex reasoning. Testing Gemini 2.5 integrations requires eval patterns that account for these unique capabilities. This
Testing
Anthropic's Computer Use API lets Claude control a computer — clicking, typing, scrolling, and taking screenshots — to complete tasks autonomously. Testing Computer Use integrations is uniquely challenging because you're testing an agentic system that interacts with a real or simulated UI. This guide covers testing strategies for
MCP
MCP servers work fine with one client. The question is whether they hold up when five AI agents are calling them simultaneously, or when a single agent fires off ten parallel tool calls in a complex workflow. Most MCP server developers never test this. They ship, an agent makes concurrent
MCP
Most MCP testing guides focus on the server side: does the server expose correct tools, does it handle errors, does it perform under load. But if you're building an application that consumes MCP servers — an AI assistant, an agent orchestrator, a developer tool — the client side needs tests
MCP
Your MCP tool works in Claude Desktop. You've called it manually a dozen times. It returns results. You're confident. Then a user sends an unexpected input format, or calls two tools in sequence with shared state, or the downstream API your tool depends on returns a
MCP
Unit tests for MCP tool handlers are necessary but not sufficient. They test the function — they don't test whether the server correctly handles the MCP protocol, whether tools are advertised correctly, whether resources are listed as expected, or whether the transport layer survives malformed messages. Integration tests do.
MCP
Most MCP server tutorials skip authentication. The examples use stdio transport with no auth, the server runs locally, and there's nothing to secure. But if you're building an MCP server that exposes real capabilities — file access, database queries, API calls — and if that server is accessible
Testing
Basic MCP server tests cover tool invocation and resource reading. Advanced patterns go further: property-based testing to discover unexpected inputs, fuzz testing for protocol robustness, multi-client concurrency testing, transport-layer validation, and performance benchmarking. This guide covers those advanced patterns. A previous guide covered the basics of testing MCP servers — unit
Testing
AI agent pipelines need CI/CD just like any software — but standard pipelines don't account for LLM non-determinism, evaluation costs, or model-level regressions. This guide covers building CI/CD pipelines specifically for AI agent systems: fast deterministic checks, LLM evaluation gates, model upgrade workflows, safety guardrails, and production
Testing
Multi-agent systems (LangGraph workflows, CrewAI crews) are dramatically harder to test than single agents. State flows between agents, handoffs can fail silently, and emergent behaviors arise from agent interactions. This guide covers testing strategies for multi-agent orchestration: state validation, handoff testing, agent isolation, and failure injection. Single agents are hard